import logging
from typing import List, Optional, Any

from .base_memory import BaseMemory
from ..agent_types import MemoryRecord

logger = logging.getLogger(__name__)

class LongTermMemory(BaseMemory):
    """
    Placeholder for long-term memory implementation.
    This could involve vector databases (like ChromaDB, Pinecone), databases, or file storage.
    """

    def __init__(self, config: Optional[dict] = None):
        logger.info("Initializing LongTermMemory (Placeholder)...")
        # TODO: Initialize connection to vector DB or other storage based on config
        self.vector_db_client = None # Placeholder
        pass

    async def add_record(self, record: MemoryRecord, task_id: Optional[str] = None) -> None:
        logger.warning("LongTermMemory.add_record not implemented.")
        # TODO: Implement adding record to long-term storage (e.g., vectorize content, store in DB)
        pass

    async def add_records(self, records: List[MemoryRecord], task_id: Optional[str] = None) -> None:
        logger.warning("LongTermMemory.add_records not implemented.")
        # TODO: Implement adding multiple records efficiently
        pass

    async def retrieve_relevant_records(
        self,
        query: str,
        task_id: Optional[str] = None,
        limit: int = 5, # Typically fewer results from LTM
        filter_criteria: Optional[dict] = None
    ) -> List[MemoryRecord]:
        logger.warning("LongTermMemory.retrieve_relevant_records not implemented.")
        # TODO: Implement relevance search (e.g., vectorize query, search vector DB)
        return []

    async def get_recent_records(
        self,
        task_id: Optional[str] = None,
        limit: int = 10,
        record_type: Optional[str] = None
    ) -> List[MemoryRecord]:
         logger.warning("LongTermMemory.get_recent_records may not be efficient or applicable. Use retrieve_relevant_records.")
         # LTM might not be optimized for purely temporal retrieval across all data.
         # This might query based on timestamps if the underlying storage supports it well.
         return []


    async def clear_task_memory(self, task_id: str) -> None:
         logger.warning("LongTermMemory.clear_task_memory might require specific implementation based on storage.")
         # Might involve deleting records with a specific task_id metadata field.
         pass

    async def clear_all_memory(self) -> None:
        logger.warning("LongTermMemory.clear_all_memory not implemented.")
        # TODO: Implement clearing the long-term storage (use with caution!)
        pass